{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  主题五：面向对象-技术知识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **应用目标**\n",
    "    1. **阶段实战：应用面向对象实现信息管理系统（CUI与GUI版本）**\n",
    "    2. **综合实战：应用面向对象来构建桌面应用程序**\n",
    "        - 计算器\n",
    "        - 记事本\n",
    "        - 扫雷\n",
    "        - GUI编程\n",
    "        - 图形图像处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-2-6b78a5bf0253>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-2-6b78a5bf0253>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    - **技能目标**\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "- **技能目标**\n",
    "    1. **能模块化编程**\n",
    "    2. **具备良好的编码规范**\n",
    "    3. **熟练的参考文档阅读能力**\n",
    "    4. **具备基本的面向对象思维与设计能力**\n",
    "    4. **基本技术应用能力**\n",
    "        - `能熟练使用面向对象的语法实现类；`\n",
    "        - `能熟练使用面向对象的语法扩展类；`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-3-29945b72f834>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-3-29945b72f834>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    - **知识点目标**\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "- **知识点目标**\n",
    "    1. **类定义与应用**\n",
    "        - `类与对象；`\n",
    "        - `类成员与调用；`\n",
    "        - `类方法与静态方法；`\n",
    "        - `数据初始化与构造器；`\n",
    "        - `数据作用域`\n",
    "        - `数据访问限制与私有`\n",
    "        - `数据与属性；`\n",
    "        - `运算符；`\n",
    "        - `成员绑定与__slots__;`\n",
    "        - `类文档注释`\n",
    "\n",
    "    2. **继承**\n",
    "        - `单重继承与多重继承；`\n",
    "        - `子类与父类数据初始化；`\n",
    "        - `override与overload；`\n",
    "        - `类型与成员测定；`\n",
    "        - `类接口；`\n",
    "        - `抽象超类；`\n",
    "\n",
    "    3. **对象的内存管理**\n",
    "        - `__new__运算符；`\n",
    "        - `析构器；`\n",
    "        - `引用计数；`\n",
    "        - `垃圾回收`\n",
    "\n",
    "    4. **元类与类装饰器**\n",
    "        - `元类类型；`\n",
    "        - `类装饰器；`\n",
    "        \n",
    "    5. **类、模块与包**\n",
    "        - `类与模块引入`\n",
    "        - `包与模块的引入；`\n",
    "        - `模块的加载与卸载；`\n",
    "        - `嵌套类`\n",
    "    6. **类设计**\n",
    "        - `类泛化关系的设计`\n",
    "        - `类关联关系的设计；`\n",
    "        - `类对象构建的设计；`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 类的定义与应用\n",
    "\n",
    "- 前面我们使用类型创建对象，其中的类型我们应该知道本质是一个类，这个类怎么实现的？这就是本主题需要讲解的内容。\n",
    "\n",
    "- 类就是类型，这是Python这种纯面向对象语言（万事万物皆对象）的核心思想：\n",
    "    - 所谓类型就是一种数据格式，是创建对象的一种内存分配格式标准（分配多大，怎么分配），甚至是数据存储序列化的格式标准（某些语言就是这样的，Python中提供\\_\\_reduce\\_\\_来描述）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类的定义与对象创建\n",
    "\n",
    "- 类的定义除了语法以外，核心工作在于两个方面：\n",
    "    - 数据定义\n",
    "    - 数据处理实现\n",
    "        - 运算符\n",
    "        - 函数操作\n",
    "        - 属性\n",
    "\n",
    "- 数据定义：\n",
    "    - 数据定义仅仅是数据的描述，Python解释器在执行的时候并不会分配内存空间。只有在使用类创建对象，解释器才会按照类中数据描述分配内存空间。\n",
    "    - 换句话说，用户定义的类与函数一样，执行的时候，仅仅是加载，不会执行，函数只有在调用才会执行；类中定义的数据只有在创建对象（调用构造器）的时候分配（有的甚至在后面调用过程中才会陆续分配空间）。\n",
    "    \n",
    "- 操作定义：\n",
    "     - 类中数据操作的定义使用的是函数，这些函数可以访问类中定义的所有数据。因为类中数据只有创建对象才能分配空间，所以这些函数也只有对象创建才能调用，通过类直接调用的时候，因为数据内存没有分配，数据不存在，调用会失败（语法上就是不允许）。\n",
    "     \n",
    "     - 不过某些函数不调用类中数据的，这些函数是可以直接调用的，为了区别其他调用类中数据的函数，这些函数会在语法上特殊标记，这些函数称为类函数，使用了类中数据的函数称为对象函数（更加标准的称呼是实例函数，因为对象也称呼为一个类的实例，因为一个类型可以反复使用，用来创建很多对象、变量或者实例）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类定义的语法\n",
    "\n",
    "\n",
    "1. 类定义语法格式\n",
    "\n",
    "```python\n",
    "\n",
    "class   类名(其他类1, ...... ):\n",
    "    语句\n",
    "    .....\n",
    "\n",
    "```\n",
    "\n",
    "2. 类的语法格式：\n",
    "    - 类头\n",
    "        - class 是关键字，与函数的def一样，表明块的类型类型时类。\n",
    "        - 类名遵循Python中标识字的命名规范。但不要与关键字冲突，也不要与标准库中的命名冲突。类名建议遵循驼峰命名法：每个单词的首字母大写（像不像驼峰？(⚈᷁‿᷇⚈᷁)）。\n",
    "        - 扩展表示类的创建不是重新定义，而是从其他类扩展定义（增加功能），这个后面会单独作为一个主题讲解。扩展的类可以是一个列表，使用逗号分隔。如果没有扩展类，默认是object类，这个可以省略()。\n",
    "        - `:` 冒号是Python中块头结束的分隔界定符\n",
    "    - 类体\n",
    "        - 最简单的类体就是一个pass空语句\n",
    "        - 类体中可以放任意语句；\n",
    "            - 数据语句（在创建对象会得到调用）\n",
    "            - 功能语句（在创建对象会得到调用）\n",
    "        - 类体中可以放任意块；\n",
    "            - 函数块\n",
    "            - 其他类块\n",
    "\n",
    "3. 最简单的类就是一个空类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.ClsA'>\n",
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__']\n"
     ]
    }
   ],
   "source": [
    "class  ClsA:   # class ClsA():  # 这是标准格式，默认是扩展object，可以省略()\n",
    "    pass\n",
    "\n",
    "print(ClsA)\n",
    "print(dir(ClsA))     # 可以看见很对成员，都是来自object。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 说明：\n",
    "    下面三个类的定义是等价的：\n",
    "\n",
    "```python\n",
    "class  ClsA:\n",
    "    pass\n",
    "\n",
    "class ClsA():\n",
    "    pass\n",
    "\n",
    "class ClsA(object):\n",
    "    pass\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 类体中的数据语句\n",
    "    - 在类体中可以定义数据语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.ClsA'>\n",
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'a']\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    a = int(20)             # 类块加载的时候得到执行\n",
    "\n",
    "print(ClsA)\n",
    "print(dir(ClsA))     #  可以看见上面定义的a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. 类体中的功能语句\n",
    "    - 在类体中可以使用功能语句；\n",
    "    - 包含流程控制块；\n",
    "    \n",
    "    - **警告：**一般情况不要在类总直接编写功能语句。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello，我是类块下的语句\n",
      "<class '__main__.ClsA'>\n",
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'a']\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    a = int(20)                                \n",
    "    print('Hello，我是类块下的语句')     #  在类块加载的时候得到执行\n",
    "\n",
    "print(ClsA)\n",
    "print(dir(ClsA))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. 类块中定义函数\n",
    "    - 类块中定义的函数在加载不会被调用，但会得到定义，定义后的函数才能调用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello，我是类块下的语句\n",
      "<class '__main__.ClsA'>\n",
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'a', 'func_a']\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    a = int(20)                                \n",
    "    print('Hello，我是类块下的语句')     #  在类块加载的时候得到执行\n",
    "    \n",
    "    def  func_a():        # 在dir的输出会看到这个定义的函数\n",
    "        print(函数)\n",
    "\n",
    "print(ClsA)\n",
    "print(dir(ClsA))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. 类块中定义类\n",
    "    - 类块中嵌套类的定义，该类也可以用来创建对象（后面专门主题讨论嵌套类的相关使用规则）\n",
    "    - 这种嵌套可以无穷无尽继续下去（理论上应该无穷无尽，不过一般解释器都有层数限制）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello，我是类块下的语句\n",
      "<class '__main__.ClsA'>\n",
      "['InnerCls', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'a', 'func_a']\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    a = int(20)                                \n",
    "    print('Hello，我是类块下的语句')     #  在类块加载的时候得到执行。\n",
    "    \n",
    "    def  func_a():        # 在dir的输出会看到这个定义的函数。\n",
    "        print(函数)\n",
    "    \n",
    "    class  InnerCls:     # 在下面dir输出可以看到InnerCls的定义。\n",
    "        pass\n",
    "\n",
    "print(ClsA)\n",
    "print(dir(ClsA))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对象创建\n",
    "\n",
    "- 从前面的应用经验中直到，使用类创建对象，需要使用类的构造器，默认情况下，没有定义构造器，类都会提供一个默认构造器`__init__()`，所以没有构造器的类，也可以用来创建对象。\n",
    "\n",
    "- 语法 \n",
    "    - 类名()     # 创建的对象不保存，没有变量名引用，称呼为匿名对象。\n",
    "    - 对象变量 = 类名()\n",
    "    \n",
    "- **重点提示：**\n",
    "    - 使用类创建对象，是不是与函数调用语法一样？**一样**！\n",
    "    - 构造器是一个特殊的函数，也称构造器函数，专门用来初始化对象（不是分配对象内存空间，分配使用的分配器函数`__new__()`，关于内存分配在对象的内存管理中专门讲解）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__main__.ClsA object at 0x107c59550>\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    pass\n",
    "\n",
    "obj_a = ClsA()\n",
    "print(obj_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类中成员与调用\n",
    "\n",
    "- 类中成员指类中定义的数据与功能：\n",
    "    - 数据成员\n",
    "    - 函数成员"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数据成员定义与使用\n",
    "\n",
    "1. 在类中直接定义数据：\n",
    "    - 与普通的数据语句一样。  变量: 类型  = 值\n",
    "\n",
    "2. 使用方式有两种：\n",
    "    - 通过类直接使用；(因为分配过空间就可以使用)\n",
    "    - 通过对象直接使用；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "88\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    m_a: int = 20\n",
    "\n",
    "\n",
    "print(ClsA.m_a)\n",
    "\n",
    "ClsA.m_a =88\n",
    "\n",
    "obj_a = ClsA()\n",
    "print(obj_a.m_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 说明：\n",
    "    - 只要分配过空间的变量就可以使用，上面使用方式很正常的。\n",
    "    - 使用ClsA.m\\_a这是成员调用运算符号，表明m\\_a在ClsA中，或者m\\_a在对象obj\\_a中。\n",
    "    - 但是上面例子中的m\\_a在对象与ClsA中是共享的，在全局作用域都可以访问（只是多了一个类前缀与对象前缀），本质是个全局变量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 函数成员定义与调用\n",
    "\n",
    "1.  类中函数的定义与普通函数的定义一样\n",
    "    - `def  函数名(参数......) -> 返回类型:`\n",
    "    \n",
    "2. 调用方式两种\n",
    "    - 类名.函数(参数)     # 无返回值\n",
    "    - 变量 = 类名.函数(参数)      # 接收返回值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    def func_a(p1:int, p2:int)->int:\n",
    "        return p1+p2\n",
    "\n",
    "print(ClsA.func_a(45,55))\n",
    "\n",
    "obj_a = ClsA()\n",
    "# print(obj_a.func_a(45,55))      # 使用对象访问产生错误"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类中类的定义域使用\n",
    "\n",
    "- 与普通类一样，只是多一个前缀（我们也称命名空间）。\n",
    "- 与函数的差异是：类也可以通过对象调用来定义该类型的对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__main__.ClsA.InnerCls object at 0x107c59908>\n",
      "<__main__.ClsA.InnerCls object at 0x107c59a20>\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    class InnerCls:\n",
    "        pass\n",
    "\n",
    "obj_inner = ClsA.InnerCls()\n",
    "\n",
    "print(obj_inner)\n",
    "\n",
    "obj_a = ClsA()\n",
    "obj_inner_a = obj_a.InnerCls()    # 使用对象作为前缀\n",
    "print(obj_inner_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 理解类中成员\n",
    "- 综上所述，类中定义的数据，函数，类与普通变量，函数，类差不多，就是多了一个命名限定符（就是使用类作为前缀`类名.`）\n",
    "\n",
    "- 我们成上述数据、函数与类为类中成员：\n",
    "    - 类中数据成员\n",
    "    - 类中函数成员\n",
    "    - 内部类（或者嵌套类）\n",
    "    \n",
    "- 类中成员的共同特征，都可以通过类直接使用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类方法与静态方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 上面类中的成员，我们一直小心翼翼称为类中的成员，而没有称为类成员是有原因的：\n",
    "    - 实际在Python语法中，存在可以通过对象与类同时调用的函数。这种函数称为静态函数，上面那种只有类能调用的函数称为类函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 静态函数与@staticmethod\n",
    "\n",
    "1. 从前面例子可以看出，类中函数默认就是类静态函数，实际可以强制标明该函数为静态函数（本质是全局函数），这个通过一个函数装饰器可以实现。\n",
    "\n",
    "    - `@staticmethod`：其定义如下：\n",
    "\n",
    "```python\n",
    "class staticmethod(object)\n",
    "     | - staticmethod(function) -> method\n",
    "\n",
    "```\n",
    "\n",
    "2. 静态函数的定义与使用例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    @staticmethod\n",
    "    def func_a(p1:int, p2:int)->int:\n",
    "        return p1+p2\n",
    "\n",
    "print(ClsA.func_a(45,55))\n",
    "\n",
    "obj_a = ClsA()\n",
    "print(obj_a.func_a(45,55))      # 运行正确"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类函数与@classmethod\n",
    "\n",
    "- 默认情况下，类中函数式类函数，也可以显式定义函数为类函数，使用装饰`@classmethod`，其定义如下：\n",
    "\n",
    "```python\n",
    "\n",
    "class classmethod(object)\n",
    "     | - classmethod(function) -> method\n",
    "\n",
    "```\n",
    "\n",
    "- 类函数必须提供一个self函数，因为类函数在解释器执行的时候，会传递一个类给函数。下面讲完self，我们还会讨论。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    @classmethod\n",
    "    def func_a(self, p1:int, p2:int)->int:     # 缺少self会错误，参数不匹配\n",
    "        return p1+p2\n",
    "\n",
    "print(ClsA.func_a(45,55))\n",
    "\n",
    "obj_a = ClsA()\n",
    "print(obj_a.func_a(45,55))      # 使用对象访问产生错误"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据作用域\n",
    "\n",
    "- 上面的数据、函数可以通过类直接使用，等同于全局变量、全局函数；这是类作为一个代码组织单位而使用，可以称为命名空间，使用场景可以用来防止与其他相同名字的数据与函数冲突。\n",
    "    - 等于对全局变量归类，使用起来更加有逻辑与调理。\n",
    "    \n",
    "- 使用类类划分数据的例子（后面因为这个系统单独引入了一个标准模块模式化这个常见问题：枚举类型）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class  Dir:\n",
    "    UP = 1\n",
    "    DOWN = 2\n",
    "    LEFT = 3\n",
    "    RIGHT = 4\n",
    "\n",
    "# 在使用方向的时候，再也不怕与其他地方定义的UP冲突了。（命名冲突是个头疼的事情）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 上面的数据只要类加载就存在，Python引入了一种数据与函数定义方式，这种数据与函数不是全局，也不是局部：\n",
    "    - 对象分配数据就分配，对象释放数据就释放；\n",
    "    - 数据与函数只有对象能够调用，类不能调用；\n",
    "    \n",
    "- 我们称这种依赖对象作为作用域的现象为：**对象域**，下面我们专门讲解对象域的语法与应用。对象域在Python中只有两个成员：\n",
    "    - 对象域数据，也称对象成员数据，简称：成员变量\n",
    "    - 对象与函数，也称对象成员函数，简称：成员函数\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 成员函数\n",
    "\n",
    "1. 定义- 在类中定义\n",
    "    - 注意其中函数第一个参数必须是self，这是一个关键字。\n",
    "\n",
    "```python\n",
    "\n",
    "class  类:\n",
    "    def   函数名(self, 参数列表，.....)->返回类型：\n",
    "        语句\n",
    "        ....\n",
    "        return 返回值     # 可选\n",
    "\n",
    "```\n",
    "\n",
    "2. 定义- 在类外定义\n",
    "    - 这是Python这种动态脚本语言的特色。\n",
    "    \n",
    "```python\n",
    "\n",
    "class  类:\n",
    "    ......\n",
    "    \n",
    "\n",
    "def 函数名(self, 参数列表)->返回类型：\n",
    "    语句\n",
    "    ......\n",
    "    return 返回值    # 可选\n",
    "\n",
    "类.函数名 = 函数名     # 绑定\n",
    "     \n",
    "```\n",
    "\n",
    "\n",
    "- 说明使用lambda表达式也可以直接绑定。\n",
    "\n",
    "\n",
    "\n",
    "3. 成员函数的调用：\n",
    "\n",
    "    - 变量 =  对象.函数(参数)\n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "4. 成员函数定义与使用的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "55\n",
      "4500\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "class  ClsA:\n",
    "    def add(self, p1, p2):\n",
    "        return p1 + p2\n",
    "    \n",
    "\n",
    "def substract(self, p1, p2):\n",
    "    return p1 - p2\n",
    "\n",
    "ClsA.substract = substract    # 动态绑定\n",
    "\n",
    "ClsA.product = lambda self, p1, p2: p1*p2     # 这里可以体会lambda表达式字面值之美。\n",
    "\n",
    "obj_a = ClsA()\n",
    "re =obj_a.add(45, 55)\n",
    "print(re)\n",
    "print(obj_a.substract(100,45))\n",
    "print(obj_a.product(100,45))\n",
    "\n",
    "# re = sClsA.add(45,55)    # 不能通过类调用\n",
    "re = ClsA.add(obj_a, 45, 55)   # 除非传递一个对象（因为成员函数，成员变量都依赖对象才能运行）\n",
    "print(re)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 特殊的构造器函数\n",
    "\n",
    "- 有一个特殊的函数，在对象定义的时候会自动调用，这就是构造器函数，其特殊之处在于:\n",
    "    - 很少显式调用，对象创建隐式自动调用（所以成为构造器函数）；\n",
    "    - 函数名固定，必须是：`__init__`；\n",
    "    - 参数与普通函数一样，可以随意；在创建对象的时候，传递相应的参数，参数使用规则与函数一样。\n",
    "    - 函数不需要返回值。\n",
    "    \n",
    "- 下面是构造器函数的使用例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "构造器： 888\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    def __init__(self, p1):\n",
    "        print('构造器：', p1)\n",
    "\n",
    "obj_a = ClsA(888)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 尽管所有成员函数都可以传递参数，但是我们建议部分重要的参数还是通过构造器函数传递是最佳的。\n",
    "- 不像其他语言，Python不支持函数重载的语法（因为可选参数，默认值可以解决其他语言导入的重载语法功能），也不支持构造器函数重载。\n",
    "- 构造器函数也用作类型转换函数。比如int类，`a = int('20',10)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 成员变量\n",
    "\n",
    "1. 成员变量语法：\n",
    "    - 语法：`self.变量 = 值`\n",
    "    - 在类中任何成员函数中都可以定义成员变量（包含构造器函数），但不能在类下面直接定义成员变量。\n",
    "    \n",
    "    - 强烈建议成员变量的定义都最好集中在构造器函数中最佳 - 一种良好的编码规范。\n",
    "    \n",
    "\n",
    "2. 定义成员量的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88 30\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    # self.m_c = 40    #  不允许在类下直接定义成员变量\n",
    "    def __init__(self):\n",
    "        self.m_a = 20\n",
    "    \n",
    "    def show(self):\n",
    "        self.m_b = 30\n",
    "\n",
    "obj_a = ClsA()\n",
    "obj_a.m_a =88                    # 修改成员变量。\n",
    "\n",
    "obj_a.show()                      # show不调用，m_b成员变量得不到定义。（这就是建议成员变量定义在构造器函数的原因）\n",
    "\n",
    "print(obj_a.m_a, obj_a.m_b)    # 访问成员变量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 关于self\n",
    "\n",
    "\n",
    "- self代表的实际是类的实例。类似于函数的参数，类没有实例化（没有使用类构建对象）时，self数不存在的；当类实例化一个对象，这个self就代表这个对象。\n",
    "\n",
    "    - 调用不同对象，self也不同。\n",
    "\n",
    "    - 在类中调用成员变量与函数的时候，就是用self表示对象。\n",
    "    \n",
    "    - 成员函数中的self与成员变量前的self是一样的作用与含义。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 再论静态函数与类函数\n",
    "\n",
    "\n",
    "- 静态函数与类函数还有同一个区别，就是对self的处理。@classmethod可以把成员函数转换成类函数。这个时候，解释器会永远给类函数的self传递类本身，没有self就不传递。\n",
    "\n",
    "下面使用例子代码说明："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.ClsA'>\n",
      "classmethod & staticmethod\n",
      "<class '__main__.ClsA'>\n",
      "classmethod & staticmethod\n",
      "display: <__main__.ClsA object at 0x107c59b00>\n"
     ]
    }
   ],
   "source": [
    "class   ClsA:\n",
    "    \n",
    "    @classmethod       # 不能使用@staticmethod\n",
    "    def show(self):       # 使用@classmethod，没self就是ClsA类本身，而不是实例本身，就算使用实例调用也是类本身\n",
    "        print(self)\n",
    "        print(\"classmethod & staticmethod\")\n",
    "        \n",
    "    def display(self):\n",
    "        print('display:', self)      # 注意这个self与上面的self不同。\n",
    "        \n",
    "\n",
    "ClsA.show()\n",
    "\n",
    "\n",
    "obj_a = ClsA()\n",
    "obj_a.show()\n",
    "obj_a.display()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **注意一**： \n",
    "    - 类下直接编写的语句，一般不具备self环境，是不能使用self的。self环境是解释器传递过来的。\n",
    "    - 说白了其实是一种实例动态绑定数据变量。\n",
    "    \n",
    "- **注意二**： \n",
    "    - 成员变量会随对象消失而消失，但是类变量本质是全局变量，不会随对象消失而消失。只要类加载了，他就存在，直到卸载类为止才消失。\n",
    "    - 遗憾的是Python在**Python3不再提供模块或者类的重新加载与卸载，除非重新启动Python**，所以大量使用类变量不是一个好的方式，这种模式只有在代码中显式使用del释放。不释放就意味着内存闲置（另外一种变相的**内存泄露**）\n",
    "    - 所有为了封装数据，最好采用成员变量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据访问限制与私有"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Python提供了一种约定（下划线）来限制对成员（成员变量，成员函数）的访问：\n",
    "    - `_xxx 不能用’from module import *’导入`\n",
    "    - `__xxx__ 系统定义名字`\n",
    "    - `__xxx 类中的私有变量名`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 私有成员变量与成员函数\n",
    "\n",
    "- 所谓私有，只能在类内部访问。在类的作用域外访问就是非法语法。下面使用例子说明："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'ClsA' object has no attribute '__show'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-623617688d55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mobj_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClsA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# print(obj_a.__m_a)     # 访问错误：AttributeError: 'ClsA' object has no attribute '__m_a'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mobj_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m             \u001b[0;31m# 访问错误：AttributeError: 'ClsA' object has no attribute '__show'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'ClsA' object has no attribute '__show'"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    def __init__(self):\n",
    "        self.__m_a =20\n",
    "    \n",
    "    def __show():\n",
    "        print('private member function!')\n",
    "\n",
    "\n",
    "obj_a = ClsA()\n",
    "# print(obj_a.__m_a)     # 访问错误：AttributeError: 'ClsA' object has no attribute '__m_a'\n",
    "obj_a.__show()             # 访问错误：AttributeError: 'ClsA' object has no attribute '__show'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 访问私有成员的场景\n",
    "\n",
    "- 下面是一个例子说明，怎么访问私有成员，类中访问：\n",
    "    - 直接访问：使用self访问；\n",
    "    - 实例访问：使用对象访问；\n",
    "\n",
    "- 下面使用的是成员变量，对成员函数一样的道理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "class  ClsA:\n",
    "    def __init__(self, v):\n",
    "        self.__m_a = v\n",
    "    \n",
    "    def is_equal(self, obj):\n",
    "        if self.__m_a == obj.__m_a:         # obj.__m_a使用实例访问私有    self.__m_a在类内部使用self直接访问\n",
    "            return True\n",
    "        else:\n",
    "            return False\n",
    "\n",
    "obj_a = ClsA(30)\n",
    "obj_b = ClsA(40)\n",
    "obj_c = ClsA(30)\n",
    "\n",
    "print(obj_a.is_equal(obj_b))\n",
    "print(obj_a.is_equal(obj_c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 访问是有类变量与类函数的例子\n",
    "\n",
    "- 对本质全局的类变量与类函数是否也一样有效呢？下面使用例子说明：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['_ClsA__cls_a', '_ClsA__func_a', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'meth_a', 'meth_b']\n",
      "20\n",
      "20\n",
      "private\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    __cls_a = 20\n",
    "    \n",
    "    def __func_a():\n",
    "        print('private')\n",
    "\n",
    "    def meth_a():\n",
    "        print(ClsA.__cls_a)       # 内部可以调用，函数一样的。\n",
    "     \n",
    "    @classmethod\n",
    "    def meth_b(self):\n",
    "        print(self.__cls_a)       # 内部可以调用，函数一样的。\n",
    "        self.__func_a();\n",
    "\n",
    "print(dir(ClsA))\n",
    "# print(ClsA.__cls_a)     # 无法调用\n",
    "ClsA.meth_a()\n",
    "ClsA.meth_b()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据与属性\n",
    "\n",
    "- 为什么计算机语言要引入这么奇怪的私有化语法，限制别人访问，这个是因为直接访问数据导致数据不安全：\n",
    "    - 比如：学生成绩，直接访问，没办法检测数据的合理性，有可能出现成绩为负数的可能性。\n",
    "    - 对函数私有化，主要不想暴露某些函数接口给用户，而是内部自己使用。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用函数访问数据\n",
    "\n",
    "- 在私有化数据的情况，建议使用函数访问数据：\n",
    "    - 修改数据\n",
    "    - 获取数据\n",
    "    \n",
    "- 下面是这种访问方式的使用例子代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Louis,0.0>\n"
     ]
    }
   ],
   "source": [
    "class Stu:\n",
    "    def __init__(self):\n",
    "        self.__name = ''\n",
    "        self.__score =0.0\n",
    "    \n",
    "    def set_name(self, name_):\n",
    "        self.__name = name_\n",
    "        \n",
    "    def get_name(self):\n",
    "        return self.__name\n",
    "    \n",
    "    def set_score(self,score_):    \n",
    "        if 0 <= score_ <= 100:         # 直接访问就没有提供处理机制\n",
    "            self.__socre=score_\n",
    "        else:\n",
    "            self.__socre=0.0\n",
    "            # 或者抛出异常，等我们学习完异常后就这么干！\n",
    "            \n",
    "    def get_score(self):\n",
    "        return self.__score\n",
    "    \n",
    "    def __str__(self):\n",
    "        return F'<{self.__name},{self.__score}>'\n",
    "    \n",
    "    \n",
    "stu = Stu()\n",
    "stu.set_name('Louis')\n",
    "stu.set_score(200)\n",
    "\n",
    "print(stu)    # 数据得到检测处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用构造器提供数据初始化\n",
    "\n",
    "- 强烈建议对成员变量首选构造器初始化方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Louis,0.0>\n",
      "<Louis,98.0>\n"
     ]
    }
   ],
   "source": [
    "class Stu:\n",
    "    def __init__(self,name_= '', score_=0.0):\n",
    "        self.__name = name_\n",
    "        if 0 <= score_ <= 100:         \n",
    "            self.__score=score_\n",
    "        else:\n",
    "            self.__score=0.0\n",
    "    \n",
    "    def set_name(self, name_):\n",
    "        self.__name = name_\n",
    "        \n",
    "    def get_name(self):\n",
    "        return self.__name\n",
    "    \n",
    "    def set_score(self, score_):    \n",
    "        if 0 <= score_ <= 100:         # 直接访问就没有提供处理机制\n",
    "            self.__score = score_\n",
    "        else:\n",
    "            self.__score = 0.0\n",
    "            # 或者抛出异常，等我们学习完异常后就这么干！\n",
    "            \n",
    "    def get_score(self):\n",
    "        return self.__score\n",
    "    \n",
    "    def __str__(self):\n",
    "        return F'<{self.__name},{self.__score}>'\n",
    "    \n",
    "    #每个类都有默认的__repr__, __str__方法，用print 实例时调用类的str方法，直接输出类的实例，调用的是类的repr方法\n",
    "stu = Stu('Louis', 200)\n",
    "print(stu)    # 数据得到检测处理\n",
    "\n",
    "stu.set_score(98.00)\n",
    "print(stu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 属性的定义与使用\n",
    "\n",
    "- 使用函数访问数据确实可以提供数据更多处理，确保数据安全，但是这种方式使用起来没有直接使用数据方便，所以某些语言提出的了属性的语法（Java就没有这种语法，C#有这种语法）\n",
    "    - 属性像函数一样可以处理数据的检测；\n",
    "    - 属性像公有成员变量一样方便使用；\n",
    "\n",
    "- 属性的语法实现依赖函数装饰器的语法，装饰器本身是函数，可以采用两种方式调用：\n",
    "    - 直接调用\n",
    "    - 装饰器调用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 属性的定义\n",
    "\n",
    "```python\n",
    "    \n",
    "    属性名 = property(fget=None, fset=None, fdel=None, doc=None) \n",
    "\n",
    "```\n",
    "- 说明：\n",
    "    - 属性定义必须定义成类数据，不能定义成成员变量。\n",
    "\n",
    "\n",
    "\n",
    "2. property的帮助：\n",
    "\n",
    "```python\n",
    "\n",
    "class property(object)\n",
    "     |-   property(fget=None, fset=None, fdel=None, doc=None) -> property attribute\n",
    "            |- fget：访问函数\n",
    "            |- fset：修改函数\n",
    "            |- fdel：释放函数\n",
    "            |- fdel：注释文档\n",
    "\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 属性的使用例子：时候在原有基础上直接添加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Louis,0.0>\n",
      "Louis\n",
      "<Jack,0.0>\n",
      "Jack\n",
      "Help on class Stu in module __main__:\n",
      "\n",
      "class Stu(builtins.object)\n",
      " |  Stu(name_='', score_=0.0)\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, name_='', score_=0.0)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __str__(self)\n",
      " |      Return str(self).\n",
      " |  \n",
      " |  get_name(self)\n",
      " |  \n",
      " |  get_score(self)\n",
      " |  \n",
      " |  set_name(self, name_)\n",
      " |  \n",
      " |  set_score(self, score_)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  name\n",
      " |      Name属性\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class Stu:\n",
    "    def __init__(self,name_= '', score_=0.0):\n",
    "        self.__name = name_\n",
    "        if 0 <= score_ <= 100:         \n",
    "            self.__score=score_\n",
    "        else:\n",
    "            self.__score=0.0\n",
    "    \n",
    "    def set_name(self, name_):\n",
    "        self.__name = name_\n",
    "        \n",
    "    def get_name(self):\n",
    "        return self.__name\n",
    "    \n",
    "    def set_score(self, score_):    \n",
    "        if 0 <= score_ <= 100:         # 直接访问就没有提供处理机制\n",
    "            self.__score = score_\n",
    "        else:\n",
    "            self.__score = 0.0\n",
    "            # 或者抛出异常，等我们学习完异常后就这么干！\n",
    "            \n",
    "    def get_score(self):\n",
    "        return self.__score\n",
    "    \n",
    "    def __str__(self):\n",
    "        return F'<{self.__name},{self.__score}>'\n",
    "    \n",
    "    # ----------------------------\n",
    "    name = property(get_name, set_name, None, \"Name属性\")     # 没有使用self，因为不需要实例\n",
    "    # ----------------------------\n",
    "    \n",
    "stu = Stu('Louis', 200)\n",
    "print(stu)    # 数据得到检测处理\n",
    "print(stu.name)\n",
    "stu.name= 'Jack'\n",
    "print(stu)\n",
    "print(stu.name)\n",
    "help(Stu)    # 查看属性名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid character in identifier (<ipython-input-5-299eb6c25df5>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-5-299eb6c25df5>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    4. 属性使用例子：装饰器方式（推荐方式，我个人喜欢上面那种）\u001b[0m\n\u001b[1;37m                                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid character in identifier\n"
     ]
    }
   ],
   "source": [
    "4. 属性使用例子：装饰器方式（推荐方式，我个人喜欢上面那种）\n",
    "\n",
    "\n",
    "\n",
    "- 遵循规则：\n",
    "    - getter属性使用：@property装饰\n",
    "    - setter属性使用：@属性名.setter装饰\n",
    "    - deleter属性使用：@属性名.deleter装饰\n",
    "    - 属性注释文档：在getter函数中定义\n",
    "    \n",
    "    - 首先使用@property定义gtter，才能定义setter等。\n",
    "    - getter，setter，deleter使用相同的函数名\n",
    "    \n",
    "    - getter,setter,deleter不是全部都必须的\n",
    "        - 只有getter称为只读属性\n",
    "        - 有setter称为可写属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<0.0,Louis>\n",
      "<0.0,Jack>\n",
      "Jack\n",
      "释放\n",
      "Help on class Stu in module __main__:\n",
      "\n",
      "class Stu(builtins.object)\n",
      " |  Stu(name_='', score_=0.0)\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, name_='', score_=0.0)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __str__(self)\n",
      " |      Return str(self).\n",
      " |  \n",
      " |  get_score(self)\n",
      " |  \n",
      " |  set_score(self, score_)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  name\n",
      " |      这是装饰器属性\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class Stu:\n",
    "    def __init__(self,name_= '', score_=0.0):\n",
    "        self.__name = name_\n",
    "        if 0 <= score_ <= 100:         \n",
    "            self.__score=score_\n",
    "        else:\n",
    "            self.__score=0.0\n",
    "    \n",
    "    @property\n",
    "    def name(self):\n",
    "        \"\"\"\n",
    "        这是装饰器属性\n",
    "        \"\"\"\n",
    "        return self.__name\n",
    "    \n",
    "    @name.setter\n",
    "    def name(self, name_):\n",
    "        self.__name = name_\n",
    "    \n",
    "    @name.deleter\n",
    "    def name(self):\n",
    "        print('释放')\n",
    "        del self.__name\n",
    "    \n",
    "    def set_score(self, score_):    \n",
    "        if 0 <= score_ <= 100:         # 直接访问就没有提供处理机制\n",
    "            self.__score = score_\n",
    "        else:\n",
    "            self.__score = 0.0\n",
    "            # 或者抛出异常，等我们学习完异常后就这么干！\n",
    "            \n",
    "    def get_score(self):\n",
    "        return self.__score\n",
    "    \n",
    "    def __str__(self):\n",
    "        return F'<{self.__score},{self.__name}>'\n",
    "\n",
    "stu = Stu('Louis', 200)\n",
    "print(stu)    # 数据得到检测处理\n",
    "\n",
    "stu.name= 'Jack'\n",
    "print(stu)\n",
    "print(stu.name)\n",
    "del stu.name\n",
    "help(Stu)    # 查看属性名"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 运算符\n",
    "\n",
    "- Python有一大特色就是支持运算符重载，每个运算符对应一个系统函数\n",
    "    - 比如：`  +  `  对应  `  __add__  `\n",
    "\n",
    "- 想实现定制的运算符，就覆盖对应的函数即可。\n",
    "\n",
    "- 下面使用两个例子说明：\n",
    "    - 给上面的Stu实现一个乘法：数乘，来对乘积调整\n",
    "    - 我们前面讲过基本类型的一个运算符@没有实现，我们可以实现一个玩玩。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 给类实现乘法运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "右乘\n",
      "<Louis,84.0>\n",
      "左乘\n",
      "<Louis,100.8>\n"
     ]
    }
   ],
   "source": [
    "class Stu:\n",
    "    def __init__(self,name_= '', score_=0.0):\n",
    "        self.__name = name_\n",
    "        if 0 <= score_ <= 100:         \n",
    "            self.__score=score_\n",
    "        else:\n",
    "            self.__score=0.0\n",
    "    \n",
    "    def set_name(self, name_):\n",
    "        self.__name = name_\n",
    "        \n",
    "    def get_name(self):\n",
    "        return self.__name\n",
    "    \n",
    "    def set_score(self, score_):    \n",
    "        if 0 <= score_ <= 100:         # 直接访问就没有提供处理机制\n",
    "            self.__score = score_\n",
    "        else:\n",
    "            self.__score = 0.0\n",
    "            # 或者抛出异常，等我们学习完异常后就这么干！\n",
    "            \n",
    "    def get_score(self):\n",
    "        return self.__score\n",
    "    \n",
    "    def del_score(self):\n",
    "        del self__score\n",
    "    \n",
    "    def __str__(self):\n",
    "        return F'<{self.__name},{self.__score}>'\n",
    "    # ----------------------------\n",
    "    def __mul__(self, value):   # 左乘（对象在左边）\n",
    "        self.__score *=value\n",
    "        print('左乘')\n",
    "        return self\n",
    "    def __rmul__(self, value):   # 左乘（对象在右边）\n",
    "        self.__score *=value\n",
    "        print('右乘')\n",
    "        return self\n",
    "    # ----------------------------\n",
    "    \n",
    "    # ----------------------------\n",
    "    name = property(get_name, set_name, None, \"Name属性\")     # 没有使用self，因为不需要实例\n",
    "    score = property(\n",
    "        fget=get_score,\n",
    "        fset=set_name, \n",
    "        fdel=del_score, \n",
    "        doc= \"Score属性\")     # 没有使用self，因为不需要实例\n",
    "    # ----------------------------\n",
    "    \n",
    "\n",
    "stu = Stu('Louis', 70)\n",
    "stu = 1.2 * stu \n",
    "print(stu)\n",
    "stu =  stu  * 1.2\n",
    "print(stu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 实现@运算符号\n",
    "\n",
    "- @符号在矩阵运算中表示内积，在矩阵中\\*表示哈马达积；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600\n"
     ]
    }
   ],
   "source": [
    "class mint(int):\n",
    "    def __matmul__(self, other):     \n",
    "        self = self * other              # 对标量，我们赋予内积就是普通乘积的运算操作\n",
    "        return self\n",
    "\n",
    "\n",
    "a = mint(20)\n",
    "b = mint(30)\n",
    "\n",
    "print(a @ b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 成员绑定与\\_\\_slots\\_\\_\n",
    "\n",
    "- 上面可以看见，类的成员是可以随意绑定的，通过类绑定是类函数与变量，通过实例绑定的是成员变量与成员函数。\n",
    "- 绑定的成员，可以通过`dir`查看，或者直接访问`__dict__`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类变量与类函数、静态函数绑定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "30\n",
      "show_2\n",
      "show_2\n",
      "show_1\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    pass\n",
    "\n",
    "# 类变量\n",
    "ClsA.a = 20\n",
    "\n",
    "print(ClsA.a)\n",
    "\n",
    "# 绑定实例变量\n",
    "obj_a = ClsA()\n",
    "obj_a.m_a = 30\n",
    "print(obj_a.m_a)\n",
    "\n",
    "# 绑定静态函数\n",
    "def show_1(self):\n",
    "    print('show_1')\n",
    "ClsA.func_1 = show_1\n",
    "\n",
    "#ClsA.func_1()\n",
    "#obj_a.func_1()\n",
    "\n",
    "\n",
    "# 绑定类函数\n",
    "@classmethod\n",
    "def show_2(self):\n",
    "    print('show_2')\n",
    "\n",
    "ClsA.func_2 = show_2\n",
    "obj_a.func_2()\n",
    "ClsA.func_2()\n",
    "obj_a.func_1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 成员变量与函数绑定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "show_1 30\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    pass\n",
    "\n",
    "# 绑定实例变量\n",
    "obj_a = ClsA()\n",
    "obj_a.m_a = 30\n",
    "print(obj_a.m_a)\n",
    "\n",
    "# 绑定静态函数\n",
    "def show_1(self):\n",
    "    print('show_1',self.m_a)\n",
    "    \n",
    "ClsA.func_1 = show_1     # 不能绑定对象（因为代码都在类中，不在函数中）\n",
    "obj_a.func_1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用`__slots__`限定属性绑定\n",
    "\n",
    "- `__slots__`用来限定属性绑定，无法限定函数的绑定，函数绑定在类代码空间的，不在对象中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "show_1 30\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    __slots__ = ['m_a','func_2']      # 你绑定其他属性试试？\n",
    "\n",
    "# 绑定实例变量\n",
    "obj_a = ClsA()\n",
    "obj_a.m_a = 30\n",
    "print(obj_a.m_a)\n",
    "\n",
    "# 绑定静态函数\n",
    "def show_1(self):\n",
    "    print('show_1',self.m_a)\n",
    "    \n",
    "ClsA.func_1 = show_1     # 不能绑定对象（因为代码都在类中，不在函数中）\n",
    "obj_a.func_1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可调用对象\n",
    "\n",
    "- Python提供了一个语法机制，可以把对象当函数调用，这种语法现象在很多框架中大量存在。因为Python万事万物皆对象，所以可调用对象有很多；\n",
    "    - 函数（内置函数，标准库函数，用户定义函数，lambda表达式）\n",
    "    - 生成器（本质也是函数）\n",
    "    - 类下面的语句\n",
    "\n",
    "- 我们指的可调用对象是指类的实例可以像函数一样调用。\n",
    "    - 可调用对象的类中必须实现`__call__`，参数根据调用的数据来确定。\n",
    "    \n",
    "- 下面是例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "对象被当成函数调用： Hello\n"
     ]
    }
   ],
   "source": [
    "class CallableCls:\n",
    "    def __call__(self, p1):\n",
    "        print('对象被当成函数调用：', p1)\n",
    "        \n",
    "\n",
    "obj_a = CallableCls()\n",
    "obj_a('Hello')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类文档注释\n",
    "\n",
    "- 在类下面的第一个字符串被当成文档注释处理，关于注释参考我们前面的知识讲解。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class ClsA in module __main__:\n",
      "\n",
      "class ClsA(builtins.object)\n",
      " |  类注释文档，就是帮助咯。\n",
      " |  \n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    \"\"\"\n",
    "    类注释文档，就是帮助咯。\n",
    "    \"\"\"\n",
    "    \n",
    "help(ClsA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 继承\n",
    "\n",
    "- 继承这个词准确的描述是扩展，在已经实现的类型基础上，扩展新的数据与功能函数。\n",
    "    - 被扩展的类称为父类\n",
    "    - 扩展的新类称为子类（有的也称诱导类）\n",
    "- Python的类的语法中提供扩展接口：\n",
    "    - `   class  类型(被扩展的类, .......):  `\n",
    "    \n",
    "- 继承最大的好处，可以在别人基础上轻松实现强大的功能\n",
    "    \n",
    "- 在继承中需要明确新增加的内容与原来的内容之间的冲突问题。\n",
    "    - 如果扩展的成员变量冲突怎么办？\n",
    "    - 如果扩展的成员函数冲突怎么办？\n",
    "    - 类变量，类函数，静态函数能否冲突？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  单重继承与多重继承\n",
    "\n",
    "- 实际上在Python中存在一个默认继承，就是所有类都默认继承object类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " #### 继承的优点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "\n",
    "    from PyQt5.QtWidgets import *\n",
    "\n",
    "    # 继承窗体，我们就可以为继承的类添加新的功能\n",
    "    class MyWidget(QWidget):\n",
    "        pass\n",
    "\n",
    "\n",
    "    app = QApplication([])\n",
    "\n",
    "    widget = MyWidget()\n",
    "    widget.show()\n",
    "\n",
    "    app.exec()\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 扩展数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 扩展数据解决如下几个问题：\n",
    "    - 命名冲突问题;\n",
    "    - 子类对象化的时候，父类的构造器怎么得到调用（父类数据也需要初始化）；\n",
    "    - 子类对象调用的冲突成员变量的时候，怎么区分？\n",
    "    \n",
    "- 下面使用例子代码说明："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 继承中构造器的问题\n",
    "\n",
    "- 子类对象化，如果没有构造器器，可以调用父类构造器。\n",
    "- 子类对象化，如果子类存在构造器，不会自动调用父类构造器，必须显式调用父类构造器：\n",
    "    - 一般建议在子类构造器中调用；\n",
    "    - 调用语法：\n",
    "        - 直接调用父类构造器函数：`父类.__init__(self, 参数......)`(不推荐：耦合性太强，直接引用)\n",
    "        - 使用super函数：\n",
    "            - 老式调用： `super(父类或者子类, self).__init_(参数......)`（推荐：耦合性地低，间接引用）\n",
    "            \n",
    "            - 新式调用：`super(父类或者子类, self).__init_(参数......)`（推荐：耦合性地低，间接引用）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.  使用父类构造器初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "父类初始化\n"
     ]
    }
   ],
   "source": [
    "\n",
    "class SuperCls:\n",
    "    def __init__(self, p_data):\n",
    "        print('父类初始化')\n",
    "        self.m_a = p_data\n",
    "\n",
    "\n",
    "class SubCls(SuperCls):\n",
    "    def show(self):\n",
    "        print(self.m_a)\n",
    "    \n",
    "        \n",
    "obj_sub = SubCls(888)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 使用子类构造器初始化父类数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "父类初始化\n",
      "父类初始化\n",
      "父类初始化\n",
      "子类初始化\n"
     ]
    }
   ],
   "source": [
    "# 子类对象怎么初始化父类中成员变量？\n",
    "class SuperCls:\n",
    "    def __init__(self):\n",
    "        print('父类初始化')\n",
    "        self.m_a = 10\n",
    "\n",
    "\n",
    "class SubCls(SuperCls):\n",
    "    def __init__(self):\n",
    "        SuperCls.__init__(self)              # 方式一：（耦合性太高，父类改名，这个也要修改）\n",
    "        super(SubCls, self).__init__()    #方式二：如果有参数带参数（Python2）\n",
    "        super().__init__()                    #  方式三：这三种方式只需要一个即可（Python3）\n",
    "        print('子类初始化')\n",
    "        self.m_a = 20\n",
    "    \n",
    "        \n",
    "obj_sub = SubCls()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### super类\n",
    "\n",
    "- super类本质是一个代理，用于创建一个对象去调用父类的方法，其工作机制是通过python描述符工作，通过mro来查找父类的 其帮助如下：\n",
    "\n",
    "```python\n",
    "class super(object)\n",
    "     |  super() -> same as super(__class__, <first argument>)\n",
    "     |  super(type) -> unbound super object\n",
    "     |  super(type, obj) -> bound super object; requires isinstance(obj, type)\n",
    "     |  super(type, type2) -> bound super object; requires issubclass(type2, type)\n",
    "```\n",
    "\n",
    "- 因为我们这里还没有掌握mro与Python描述符的概念，所以不做深入的分析，能掌握应用方式即可。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### super与实例\n",
    "\n",
    "1.  super()不是对象实例，实际上属性访问的代理，通过属性描述符访问。\n",
    "    \n",
    "    - Python中属性的访问是通过对象字典`__dict__`来查找，对象的字典`__dict__`与类型的字典是有区别的。\n",
    "        - 对象字典存放实例的属性与方法；\n",
    "        - 类型字典存放类的属性与方法；\n",
    "        \n",
    "\n",
    "\n",
    "- 下面是一个例子代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'m_a': 20}\n",
      "{'__module__': '__main__', 'cls_a': 10, '__init__': <function ClsA.__init__ at 0x000001675C7B59D8>, 'propa': <property object at 0x000001675C7B4B38>, 'show': <function ClsA.show at 0x000001675C7B5C18>, 'display': <classmethod object at 0x000001675C7B3948>, 'print': <staticmethod object at 0x000001675C7B3C48>, '__dict__': <attribute '__dict__' of 'ClsA' objects>, '__weakref__': <attribute '__weakref__' of 'ClsA' objects>, '__doc__': None}\n"
     ]
    }
   ],
   "source": [
    "class ClsA:\n",
    "    cls_a = 10\n",
    "    def __init__(self):\n",
    "        self.m_a = 20\n",
    "        \n",
    "    @property\n",
    "    def propa(self):\n",
    "        return self.m_a\n",
    "    \n",
    "    @propa.setter\n",
    "    def propa(self, value):\n",
    "        self.m_a = value\n",
    "    \n",
    "    @propa.deleter\n",
    "    def propa(self):\n",
    "        del self.m_a\n",
    "        \n",
    "    def show(self):\n",
    "        print(\"member method\")\n",
    "        \n",
    "    @classmethod\n",
    "    def display(self):\n",
    "        print('class method')\n",
    "    \n",
    "    @staticmethod\n",
    "    def print():\n",
    "        print('static method')\n",
    "\n",
    "obj_a = ClsA()\n",
    "print(obj_a.__dict__)\n",
    "print(ClsA.__dict__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 属性的访问过程\n",
    "    - 首先方法对象的字典\n",
    "    - 类型的字典\n",
    "    - 类型父类的字典（元类除外）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "member method\n",
      "class method\n",
      "static method\n"
     ]
    }
   ],
   "source": [
    "print(obj_a.m_a)   # 直接从对象字典访问\n",
    "obj_a.show()        # 先从对象字典，然后从类型字典访问\n",
    "obj_a.display()     # 先从对象字典，然后从类型字典访问\n",
    "\n",
    "ClsA.print()          # 直接从类型字典访问"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 使用字典访问属性\n",
    "    - 其中存在三个Python类型：\n",
    "        - function  ： 动态生成，不能使用help(function)查看帮助。\n",
    "        - staticmethod：查看帮助help(staticmethod)\n",
    "        - classmethod：查看帮助help(classmethod)\n",
    "   - 上述三个类型有一个共同的函数，可以得到对象的实例。\n",
    "       -  `__get__(self, instance, owner, /)`\n",
    "           - instance : 实例：可以是类型的实例-对象，也可以是元类的实例-类型。\n",
    "           \n",
    "\n",
    "\n",
    "- **说明**：\n",
    "    - 关于更多的类的内部访问与实现机制，这里不深入讲述，在类的高级编程技巧中会详细描述；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "10\n",
      "member method\n",
      "member method\n",
      "member method\n",
      "<classmethod object at 0x000001DD166FF388>\n",
      "class method\n",
      "class method\n",
      "<staticmethod object at 0x000001DD166FFDC8>\n",
      "static method\n",
      "属性--------\n",
      "<property object at 0x000001DD1665FBD8>\n",
      "20 <class 'int'>\n",
      "88\n"
     ]
    }
   ],
   "source": [
    "print(obj_a.__dict__['m_a'])     # 对象字典\n",
    "print(ClsA.__dict__['cls_a'])        # 类型字典\n",
    "\n",
    "# 成员函数\n",
    "# help(type(ClsA.__dict__['show']))\n",
    "ClsA.__dict__['show'](obj_a)\n",
    "ClsA.__dict__['show'].__call__(obj_a)\n",
    "\n",
    "ClsA.__dict__['show'].__get__(obj_a)  # 得到函数\n",
    "ClsA.__dict__['show'].__get__(obj_a)()\n",
    "\n",
    "# 类函数\n",
    "print(ClsA.__dict__['display'])\n",
    "# ClsA.__dict__['display'](ClsA)\n",
    "ClsA.__dict__['display'].__get__(ClsA,ClsA)  # 得到函数\n",
    "ClsA.__dict__['display'].__get__(ClsA,ClsA)()\n",
    "ClsA.__dict__['display'].__get__(obj_a,ClsA)  # 得到函数\n",
    "ClsA.__dict__['display'].__get__(obj_a,ClsA)()\n",
    "\n",
    "# 静态函数\n",
    "print(ClsA.__dict__['print'])\n",
    "ClsA.__dict__['print'].__get__(ClsA,ClsA)  # 得到函数\n",
    "ClsA.__dict__['print'].__get__(ClsA,ClsA)()\n",
    "\n",
    "print('属性--------')\n",
    "print(ClsA.__dict__['propa'])\n",
    "p = ClsA.__dict__['propa'].__get__(obj_a,ClsA)\n",
    "print(p, type(p))\n",
    "ClsA.__dict__['propa'].__set__(obj_a,88)\n",
    "print(obj_a.propa)\n",
    "# 删除\n",
    "ClsA.__dict__['propa'].__delete__(obj_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 扩展功能\n",
    "\n",
    "    \n",
    "- 扩展功能应该解决几个问题：\n",
    "    - 新的函数命名相同的问题\n",
    "        - 同一个类不允许命名冲突，但父子类中函数名可以相同。\n",
    "    - 子类访问父类中成员变量的语法\n",
    "        - 父类与子类成员变量不冲突，直接访问即可；\n",
    "        - 父类与子类成员变量冲突，直接访问，子类优先；\n",
    "        - 父类与子类成员变量冲突，想访问父类成员变量，使用super关键字。\n",
    "        \n",
    "- 下面使用例子代码来说明："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "super_init\n",
      "sub_init\n",
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'show']\n",
      "20\n",
      "Sub\n",
      "Super\n"
     ]
    }
   ],
   "source": [
    "class SuperCls:\n",
    "    def __init__(self):\n",
    "        print('super_init')\n",
    "        self.m_a = 10\n",
    "\n",
    "    def show(self):\n",
    "        print('Super')\n",
    "\n",
    "\n",
    "class SubCls(SuperCls):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        print('sub_init')\n",
    "        self.m_a = 20\n",
    "    \n",
    "    def show(self):\n",
    "        print('Sub')\n",
    "    \n",
    "    def call_super(self):\n",
    "#         print(super(SuperCls, self).m_a)\n",
    "        print(dir(SuperCls))\n",
    "        print(self.m_a)\n",
    "        self.show()\n",
    "        super().show()\n",
    "\n",
    "\n",
    "obj_sub = SubCls()\n",
    "obj_sub.call_super()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多重继承\n",
    "#### 多重继承的问题\n",
    "    - 下面例子说明多重继承的问题，实际多重的继承的目的是解决功能扩展，不是数据扩展。\n",
    "    - 而且多重继承还要遵循一定的编程模式。\n",
    "    \n",
    "    \n",
    "\n",
    "- 下面是一个实际应用场景中的多重继承产生的问题例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'MyWidget' object has no attribute 'start'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-b4d080aef042>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mapp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mQApplication\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mwidget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMyWidget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mapp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-47-b4d080aef042>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, parent)\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMyWidget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMyWidget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'MyWidget' object has no attribute 'start'"
     ]
    }
   ],
   "source": [
    "from PyQt5.QtWidgets import *\n",
    "from PyQt5.QtCore import *\n",
    "\n",
    "\n",
    "class MyWidget(QWidget, QThread):\n",
    "    idx = 0\n",
    "\n",
    "    def __init__(self, parent=None):\n",
    "        super(MyWidget, self).__init__(parent=parent)\n",
    "        super(MyWidget, self).__init__(parent=parent)\n",
    "        self.start()\n",
    "        self.show()\n",
    "\n",
    "    def run(self):\n",
    "        while True:\n",
    "            self.setWindowTitle(F'{MyWidget.idx:08d}')\n",
    "            MyWidget.idx += 1\n",
    "            QThread.msleep(100)\n",
    "\n",
    "\n",
    "app = QApplication([])\n",
    "widget = MyWidget()\n",
    "\n",
    "app.exec()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 下面例子使用典型的钻石结构来说明多重继承中的问题与编程注意的问题。\n",
    "    - 数据扩展\n",
    "    - 功能扩展\n",
    "    \n",
    "- 下面是后面例子中使用的继承结构：\n",
    "    \n",
    "```\n",
    "        A\n",
    "      /  \\\n",
    "     /    \\\n",
    "    B     C\n",
    "    \\      /\n",
    "      \\   /\n",
    "        D\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 多重继承中的构造器调用\n",
    "\n",
    "1. 多重继承中的构造器属于方法，Python采用方法解析顺序管理，每个类中提供一个__mro__来管理，使用super代理，super代理就按照MRO管理顺序调用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n",
      "C\n",
      "B\n",
      "D\n",
      "10,20,30,40\n"
     ]
    }
   ],
   "source": [
    "class  A:\n",
    "    def __init__(self):\n",
    "        self.m_a = 10\n",
    "        print('A')\n",
    "\n",
    "class B(A):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.m_b = 20\n",
    "        print('B')\n",
    "\n",
    "class C(A):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.m_c = 30\n",
    "        print('C')\n",
    "\n",
    "class D(B,C):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.m_d = 40\n",
    "        print('D')\n",
    "\n",
    "obj_d = D()\n",
    "print(obj_d.m_a,obj_d.m_b,obj_d.m_c,obj_d.m_d,sep=',')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 使用传统方式处理多重继承中的构造器调用\n",
    "\n",
    "\n",
    "- 传统构造器的调用，会导致父类构造器重复调用，但是可能更加直观。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n",
      "B\n",
      "A\n",
      "C\n",
      "D\n",
      "10,20,30,40\n"
     ]
    }
   ],
   "source": [
    "class  A:\n",
    "    def __init__(self):\n",
    "        self.m_a = 10\n",
    "        print('A')\n",
    "\n",
    "class B(A):\n",
    "    def __init__(self):\n",
    "        A.__init__(self)\n",
    "        self.m_b = 20\n",
    "        print('B')\n",
    "\n",
    "class C(A):\n",
    "    def __init__(self):\n",
    "        A.__init__(self)\n",
    "        self.m_c = 30\n",
    "        print('C')\n",
    "\n",
    "class D(B,C):\n",
    "    def __init__(self):\n",
    "        B.__init__(self)\n",
    "        C.__init__(self)\n",
    "        self.m_d = 40\n",
    "        print('D')\n",
    "\n",
    "obj_d = D()\n",
    "print(obj_d.m_a,obj_d.m_b,obj_d.m_c,obj_d.m_d,sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 多重继承中数据扩展的冲突\n",
    "\n",
    "- 因为多重继承，两个父类中方法、属性、成员变量可能会产生冲突；在语法上，这种冲突是允许的，所以我们在编程中需要注意，形成自己的编程技巧。\n",
    "\n",
    "- 成员变量冲突的编程在新老调用方式上的注意事项："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 老式调用方式，覆盖方式由调用顺序决定。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n",
      "C\n",
      "A\n",
      "B\n",
      "D\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "class  A:\n",
    "    def __init__(self):\n",
    "        self.m_a = 10\n",
    "        print('A')\n",
    "\n",
    "class B(A):\n",
    "    def __init__(self):\n",
    "        A.__init__(self)\n",
    "        self.m_a = 20\n",
    "        print('B')\n",
    "\n",
    "class C(A):\n",
    "    def __init__(self):\n",
    "        A.__init__(self)\n",
    "        self.m_a = 30\n",
    "        print('C')\n",
    "\n",
    "class D(B,C):\n",
    "    def __init__(self):\n",
    "        C.__init__(self)\n",
    "        B.__init__(self)\n",
    "        \n",
    "        # self.m_a = 40\n",
    "        print('D')\n",
    "\n",
    "obj_d = D()\n",
    "print(obj_d.m_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 新式的覆盖由继承的顺序决定；继承顺序是从后往前。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n",
      "B\n",
      "C\n",
      "D\n",
      "40\n"
     ]
    }
   ],
   "source": [
    "class  A:\n",
    "    def __init__(self):\n",
    "        self.m_a = 10\n",
    "        print('A')\n",
    "\n",
    "class B(A):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.m_a = 20\n",
    "        print('B')\n",
    "\n",
    "class C(A):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.m_a = 30\n",
    "        print('C')\n",
    "\n",
    "class D(C,B):      # 继承的先后，继承顺序是从后往前\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.m_a = 40\n",
    "        print('D')\n",
    "\n",
    "obj_d = D()\n",
    "print(obj_d.m_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 这种语法机制本身还是存在漏洞的，如果扩展函数的时候，容易产生不一致的情况。B中的函数可能调用C中的成员变量。\n",
    "- 下面我们来看看函数扩展的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 多重继承中的功能扩展"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A的方法： 40\n"
     ]
    }
   ],
   "source": [
    "class  A:\n",
    "    def __init__(self):\n",
    "        self.m_a = 10\n",
    "\n",
    "    def show(self):\n",
    "        print('A的方法：',self.m_a)\n",
    "\n",
    "class B(A):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.m_a = 20\n",
    "\n",
    "#     def show(self):\n",
    "#         print(self.m_a)\n",
    "\n",
    "class C(A):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.m_a = 30\n",
    "\n",
    "#     def show(self):\n",
    "#         print(self.m_a)\n",
    "\n",
    "class D(C,B):      # 继承的先后，继承顺序是从后往前\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.m_a = 40\n",
    "#     def show(self):\n",
    "#         print(self.m_a)\n",
    "\n",
    "obj_d = D()\n",
    "obj_d.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 调用是A中定义的成员方法，实际上该方法使用的是D类的数据，因为这种多重继承中的覆盖，导致数据一些数据环境产生改变，从而导致运行问题。\n",
    "\n",
    "- 该怎么使用多重继承？一般根据经验，建议主要数据按照主线继承，需要扩展的方法，采用多重继承来实现，而且方法中使用的数据尽量不要冲突覆盖，这种编程模式称为继承组合模式【Mixin】（区别于依赖、聚合与组合关系）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类型与成员测定\n",
    "\n",
    "- Python提供了两个函数来对对象进行常见的测定：\n",
    "    - 获取类型\n",
    "        - type\n",
    "    - 实例的类型判定\n",
    "        - isinstance    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### type类型\n",
    "\n",
    "- type说明\n",
    "\n",
    "```python\n",
    "class type(object)\n",
    "     |  type(object_or_name, bases, dict)\n",
    "     |  type(object) -> the object's type\n",
    "     |  type(name, bases, dict) -> a new type\n",
    "```\n",
    "\n",
    "- 使用type创建类型在下面介绍，但是使用type可以返回对象的类型，该类型还可以继续创建对象。这是Python万事万物皆对象的理念，类型也是对象，类型的类型就是type，这是元类的概念。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255\n",
      "<class 'int'>\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "a = int('FF', 16)\n",
    "print(a)\n",
    "cls = type(a)\n",
    "print(cls)\n",
    "\n",
    "b = cls('0X0F', 0)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### isinstance运算符\n",
    "\n",
    "- isinstance函数定义\n",
    "\n",
    "```\n",
    "isinstance(obj, class_or_tuple, /)\n",
    "    Return whether an object is an instance of a class or of a subclass thereof.\n",
    "```\n",
    "\n",
    "- 参数说明:\n",
    "    - obj：一个实例对象\n",
    "    - class_or_tuple：类型或者类型元组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "class  A:\n",
    "    pass\n",
    "\n",
    "class B(A):\n",
    "    pass\n",
    "\n",
    "class C(A):\n",
    "    pass\n",
    "\n",
    "class D(C,B): \n",
    "    pass\n",
    "\n",
    "obj_d = B()\n",
    "bl = isinstance(obj_d, (C, D))\n",
    "print(bl)\n",
    "bl = isinstance(obj_d, (A, B, C, D))\n",
    "print(bl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类接口与抽象类\n",
    "\n",
    "- 在很多语言中，有一种特殊的继承：接口，接口是为了降低类之间耦合度而提出的语法设计。但是在Python这种弱类型语言，接口的语法在Python中基本上没有意义。\n",
    "\n",
    "- 但是接口还具有另外一个应用需求：规范调用的方法\n",
    "    - 强制子类覆盖父类的方法，从而强制要求子类遵循父类定义的规范。\n",
    "\n",
    "- 抽象类：\n",
    "    - 定义方法规范的类称为接口或者抽象类，在Python中称呼抽象类会更加规范。因为接口与抽象类的在传统意义上还有有一些区别，接口是纯方法规范，抽象类一般是传统实现方法与规范接口方法混合。因为Python语法的特点，纯粹的接口规范类不存在。\n",
    "   \n",
    "- 抽象类的定义\n",
    "    - 抽象类的接口方法定义使用abc模块中的abstractmethod函数装饰器;\n",
    "    - 同时需要指定元类为abc.ABCMeta\n",
    "    \n",
    "```\n",
    "abstractmethod(funcobj)\n",
    "    A decorator indicating abstract methods.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-77-5cd5007a98d3>, line 14)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-77-5cd5007a98d3>\"\u001b[0;36m, line \u001b[0;32m14\u001b[0m\n\u001b[0;31m    obj = ImplementionCls()\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "import abc\n",
    "\n",
    "class AbstractCls(metaclass=abc.ABCMeta):\n",
    "    @abc.abstractmethod\n",
    "    def meth(self):\n",
    "        pass\n",
    "    \n",
    "\n",
    "class  ImplementionCls(AbstractCls):\n",
    "#      def meth(self):\n",
    "#         pass\n",
    "\n",
    "# obj = AbstractCls()\n",
    "obj = ImplementionCls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 如果不实现抽象方法，则子类也是抽象类，抽象类是不能实例化的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 抽象超类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对象的内存管理\n",
    "\n",
    "- 因为Python使用虚拟机，实现自动内存管理，开发者实际上一般情况下是不需要关心内存的管理啊的。我们这里讲的对象的内存管理主要是对象的分配与释放。实际上在编程中还有很多内存相关的细节，这里不详细描述，比如：对bit的操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \\_\\_new\\_\\_运算符\n",
    "\n",
    "- 一般我们很少重载这个运算符，但在某些场合还是会使用new运算符，更主要的是要区别`__new__`与`__init__`。\n",
    "\n",
    "    - `__new__`负责内存分配；`__new__`是静态的！\n",
    "    - `__init__`负责数据初始化；在`__new__`调用后调用！\n",
    "    \n",
    "\n",
    "- 实际上Python不提供直接的内存分配，在object中提供一个内存分配的封装，当类没有提供`__new__`运算符的时候，都会调用父类的`__new__`，父类没有重载，在会一直逆源到object的`__new__`运算符。\n",
    "\n",
    "- 但是Python封装提供了一些内存相关的操作函数，在今后的学习中会逐步涉及。\n",
    "\n",
    "- 下面是`__new__`运算符重载的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "内存分配器\n",
      "构造器\n"
     ]
    }
   ],
   "source": [
    "class ClsA(object):\n",
    "    def __init__(self):\n",
    "        print('构造器')\n",
    "        \n",
    "    def __new__(cls, *args, **kwargs):\n",
    "        print('内存分配器')  #  类型\n",
    "        return object.__new__(cls, *args, **kwargs)   \n",
    "    \n",
    "\n",
    "obj_a = ClsA()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 析构器\n",
    "\n",
    "- python也提供析构机制。\n",
    "    - Python的析构也是使用运算符`__del__`。\n",
    "    - `__del__`在对象释放时自动调用（垃圾回收器自动工作），或者调用del释放；主要用来提供对象内部的一些资源释放。比如在对象释放前：关闭网络，释放成员变量等。\n",
    "    \n",
    "\n",
    "- 下面是析构器使用例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "释放\n"
     ]
    }
   ],
   "source": [
    "class ClsA(object):\n",
    "    def __del__(self):\n",
    "        print('释放')\n",
    "\n",
    "\n",
    "obj_a = ClsA()\n",
    "del obj_a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 引用计数\n",
    "\n",
    "- Python的sys模块提供一个对象被引用的次数：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "class  A:\n",
    "    pass\n",
    "\n",
    "obj=A()\n",
    "print(sys.getrefcount(obj))\n",
    "#计数是2。与Python解释器有关\n",
    "#如果两个对象交叉引用，则很难释放，这时候垃圾回收器开始作用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 垃圾回收\n",
    "\n",
    "- Python 采用垃圾回收机制来清理不再使用的对象；Python 提供gc模块释放不再使用的对象，Python 采用‘引用计数’ 的算法方式来处理回收，即：当某个对象在其作用域内不再被其他对象引用的时候，Python 就自动清除对象；Python 的函数collect()可以一次性收集所有待处理的对象(gc.collect())\n",
    "\n",
    "- gc模块说明：\n",
    "\n",
    "```python\n",
    "\n",
    "    enable() -- Enable automatic garbage collection.\n",
    "    disable() -- Disable automatic garbage collection.\n",
    "    isenabled() -- Returns true if automatic collection is enabled.\n",
    "    collect() -- Do a full collection right now.\n",
    "    get_count() -- Return the current collection counts.\n",
    "    get_stats() -- Return list of dictionaries containing per-generation stats.\n",
    "    set_debug() -- Set debugging flags.\n",
    "    get_debug() -- Get debugging flags.\n",
    "    set_threshold() -- Set the collection thresholds.\n",
    "    get_threshold() -- Return the current the collection thresholds.\n",
    "    get_objects() -- Return a list of all objects tracked by the collector.\n",
    "    is_tracked() -- Returns true if a given object is tracked.\n",
    "    get_referrers() -- Return the list of objects that refer to an object.\n",
    "    get_referents() -- Return the list of objects that an object refers to.\n",
    "    \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 1, 1)\n",
      "__init__\n",
      "2\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "__del__\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/python\n",
    "#coding=utf-8\n",
    "#垃圾回收器，gc模块\n",
    "\n",
    "import  gc,sys\n",
    "gc.enable()\n",
    "print(gc.get_threshold())   #垃圾对象分代\n",
    "gc.set_threshold(2,1,1)\n",
    "class A:\n",
    "    def __init__(self):\n",
    "        print(\"__init__\")\n",
    "    def __del__(self):\n",
    "        print(\"__del__\")\n",
    "\n",
    "a=A()\n",
    "print(sys.getrefcount(a))\n",
    "\n",
    "b=a\n",
    "c=b\n",
    "d=c\n",
    "\n",
    "del a\n",
    "gc.collect()\n",
    "print(gc.garbage)\n",
    "del b\n",
    "gc.collect()\n",
    "print(gc.garbage)\n",
    "del c\n",
    "print (gc.garbage)\n",
    "del d\n",
    "#gc.collect()\n",
    "print(gc.garbage)\n",
    "#垃圾回收主要寻找循环引用并决定释放"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 元类与类装饰器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  元类类型\n",
    "\n",
    "- 前面我们介绍过使用对象来得到type实例（类），从而判定对象的类型，实际上使用type构造类型。这种能构造类的类型就是元类（就是type类）。\n",
    "\n",
    "\n",
    "- type的帮助：\n",
    "\n",
    "```\n",
    "class type(object)\n",
    "     |  type(object_or_name, bases, dict)\n",
    "     |  type(name, bases, dict) -> a new type\n",
    "```\n",
    "\n",
    "- 说明：\n",
    "    - object_or_name 对象与对象名（字符串）\n",
    "    - bases父类（元组类型）\n",
    "    - dict ：字典属性（字典类型）\n",
    "\n",
    "- 下面是使用元类创建类的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "cls_name\n"
     ]
    }
   ],
   "source": [
    "class A:\n",
    "    pass\n",
    "\n",
    "class B:\n",
    "    pass\n",
    "\n",
    "TypeCls = type('cls_name', (A, B), {'m_a': 20})\n",
    "\n",
    "a = TypeCls()\n",
    "print(a.m_a)\n",
    "print(TypeCls.__name__)\n",
    "                                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定制元类\n",
    "\n",
    "- type是Python解释器内置的元类，用户可以实现自己的元类；\n",
    "    - 继承type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Meta(type):\n",
    "    \n",
    "    def __new__(meta, *args, **kwargs):\n",
    "        print( '创建类：')\n",
    "        print( '\\t*args：', args)\n",
    "        print( '\\t**kwargs：', kwargs)\n",
    "        return super().__new__(meta, *args)\n",
    "\n",
    "    def __init__(cls, *args, **kwargs):\n",
    "        print('类型初始化')\n",
    "        print( '\\t*args：', args)\n",
    "        print( '\\t**kwargs：', kwargs)\n",
    "        super().__init__(*args)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用定制元类定义类\n",
    "\n",
    "- 使用metaclass指定元类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "创建类：\n",
      "\t*args： ('ClsA', (), {'__module__': '__main__', '__qualname__': 'ClsA', '__init__': <function ClsA.__init__ at 0x10fe83ea0>})\n",
      "\t**kwargs： {'p1': '参数值'}\n",
      "类型初始化\n",
      "\t*args： ('ClsA', (), {'__module__': '__main__', '__qualname__': 'ClsA', '__init__': <function ClsA.__init__ at 0x10fe83ea0>})\n",
      "\t**kwargs： {'p1': '参数值'}\n",
      "创建对象\n"
     ]
    }
   ],
   "source": [
    "class ClsA(metaclass=Meta, p1='参数值'):\n",
    "\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        print('创建对象')\n",
    "\n",
    "\n",
    "        \n",
    "obj_a = ClsA()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类装饰器\n",
    "\n",
    "- 上面通过元类类控制类的定义，实际也可以通过类装饰器来实现。\n",
    "- 类装饰器本质是函数，参数是类。\n",
    "    - 在装饰器函数中，可以对类进行处理，比如增加一些字典，属性等。\n",
    "    \n",
    "- 使用装饰器与函数一样，差别在于类装饰器使用在类的前面。\n",
    "    - `@ 装饰器名`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "def decorate(cls):\n",
    "    cls.cls_a = 20\n",
    "    return cls\n",
    "\n",
    "\n",
    "@decorate\n",
    "class  ClsA:\n",
    "    pass\n",
    "\n",
    "\n",
    "print(ClsA.cls_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用UML描述类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 类图\n",
    "\n",
    "1.  工具：\n",
    "    - `https://www.processon.com`"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 类图的UML表示\n",
    "    ![image.png](attachment:image.png)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 类图说明\n",
    "    - 区域说明：\n",
    "        - `类名：指定类名;`\n",
    "        - `数据成员：定义成员变量;`\n",
    "        - `函数成员：定义成员函数;`\n",
    "        \n",
    "    -符号说明：\n",
    "        - `+ 表示public；`\n",
    "        - `- 表示private；`\n",
    "        - `# 表示protected；`\n",
    "    \n",
    "    - 成员说明：\n",
    "        - 成员变量说明：`成员变量名：类型 = 默认值`\n",
    "        - 成员函数说明：`函数名(参数：类型，......)：返回类型`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 类关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 关联关系"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单向关联\n",
    "\n",
    "- 一般用来描述实体关系\n",
    "    - 实体ClsA 有 ClsB作为成员；\n",
    "    \n",
    "    - 比如：User类都有一个地址Address类型的数据；\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 双向关联\n",
    "\n",
    "- 一般用来描述实体类关系：\n",
    "    - 表示双向的拥有关系；\n",
    "    \n",
    "    - 比如：User类都有一个购买Product列表；某个Product被哪个User购买；\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 自关联\n",
    "- 一般用来描述实体；\n",
    "    - 自己拥有自己；\n",
    "        - 比如数据结构的链表节点，就是自己拥有自己；\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 依赖关系\n",
    "\n",
    "- 一般用来描述调用关系\n",
    "    - ClsA的call需要ClsB的called才能工作；\n",
    "    - 一般是一个类的函数参数使用了另外一个类型的对象；\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 聚合关系\n",
    "\n",
    "- 一般用来描述调用关系\n",
    "    -  ClsA的实现需要ClsB；\n",
    "    - 并且ClsB作为ClsA的成员变量，但是ClsB对象的构建不在ClsA中构建，ClsB对象可以独立存在，不会随ClsA对象的释放而释放。\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 组合关系\n",
    "\n",
    "- 一般用来描述调用关系\n",
    "    -  ClsA的实现需要ClsB；\n",
    "    - 并且ClsB作为ClsA的成员变量，ClsB对象的构建在ClsA中构建；\n",
    "    - ClsB对象不是独立存在的，ClsA对象释放时，ClsB对象也会被释放。\n",
    "    \n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 继承关系\n",
    "\n",
    "- 继承关系是面向对象中最基本的关系了；\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 接口继承关系\n",
    "\n",
    "- 这个关系在Python非常淡化，因为Python在语法上是弱类型语言（因为类型是动态判定的）；\n",
    "    - 但是在Python从编程技巧上，可以实现与接口类似的程序结构。\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "318px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
